Sample Syllabus

Course Description

This course, Ethics in Data Science (DS330), explores ethical considerations in data science, including privacy, bias, fairness, and transparency. Data science has the power to impact individuals, communities, and societies, and ethical decision-making is crucial in ensuring that data science practices are responsible and beneficial. In this course, students will examine case studies and ethical frameworks to develop critical thinking skills for ethical decision-making in data science.

Learning Objectives

By the end of this course, students will be able to:

• Understand ethical considerations in data science, including privacy, bias, fairness, and transparency.
• Analyze case studies related to ethical issues in data science.
• Apply ethical frameworks to analyze and address ethical dilemmas in data science.
• Develop critical thinking skills for ethical decision-making in data science.

Course Structure

The course content will be presented through a series of lectures, case studies, and group discussions. Students will be evaluated through a final essay and peer feedback.

Draft Submissions

Throughout the course, students will submit drafts of their final essay for peer review. This process will allow students to receive feedback from their peers and refine their ideas and arguments.

Peer Feedback

Students will also be required to provide feedback on the drafts of their peers. This process will help students develop their critical thinking and analytical skills by evaluating and providing constructive feedback on their peers’ work.

Final Essay

The final assessment for this course will be an essay on a topic related to ethics in data science. The essay will require students to apply the ethical frameworks and critical thinking skills learned throughout the course to analyze and address an ethical dilemma in data science. The final essay will count towards a significant portion of the overall course grade.

Schedule

The following is a general outline of the topics covered in the course:

Week	Topic
1	Introduction to Ethics in Data Science
2	Privacy and Data Protection
3	Bias and Fairness in Machine Learning
4	Transparency and Accountability in Data Science
5	Ethical Decision-Making Frameworks
6	Case Studies in Ethical Issues in Data Science
7	Ethical Issues in Data Collection and Use
8	Ethical Issues in Data Visualization and Communication
9	Ethical Issues in Data Science Research
10	Ethics in Artificial Intelligence
11	Responsible AI and Governance
12	Course Reflections